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Article
Adaptive control of nonlinear time-varying processes using selective recursive kernel learning method
Industrial and Engineering Chemistry Research (2011)
  • Yi Liu
  • Wenlu Chen
  • Zengliang Gao
  • Haiqing Wang, Zhejiang University
  • Ping Li, Zhejiang University
Abstract

A selective recursive kernel learning-based (SRKL) adaptive predictive controller is proposed for nonlinear time-varying processes. First, a SRKL identification model is presented with an efficient sparsification strategy which makes a trade-off between the tracking precision and the controller’s complexity. The SRKL model can be updated efficiently by introducing and/or deleting a sample via recursive learning algorithms. Consequently, the model can adjust its structure adaptively to capture the process dynamics and time-varying characteristics. On the basis of the SRKL model, a predictive controller with an adaptive modification item is designed. The novel controller can achieve better performance since the SRKL model can trace the process characteristics online. The obtained results on a laboratory-scale liquid-level process and a continuous bioreactor with time-varying parameters show that the proposed controller is superior to the traditional proportional-integral-derivative (PID) controller and related controller with an offline KL model without online updating.

Publication Date
Spring March 1, 2011
Citation Information
Yi Liu, Wenlu Chen, Zengliang Gao, Haiqing Wang, et al.. "Adaptive control of nonlinear time-varying processes using selective recursive kernel learning method" Industrial and Engineering Chemistry Research Vol. 50 Iss. 5 (2011)
Available at: http://works.bepress.com/inter_liu/8/